Abstract Assistive devices such as augmentative and alternative communication (AAC) systems are used by people with communication and motor disabilities, such as amyotrophic lateral sclerosis (ALS, commonly known as Lou Gehrig?s disease), to communicate and interact with their environment. There are various commercially available AAC devices that are controlled by access methods such as touch, switch, head tracking and eye gaze; however, these access methods become difficult or impossible to use when sustained muscle control is more challenging or voluntary motor control is lost. There are brain-computer interface (BCI) communication systems, such as the P300 speller, that use sensory stimulation to elicit and then detect sensory neural responses in electroencephalography (EEG) data, and these communication aids do not require any motor control on the part of the affected individual. However, the accuracies and spelling speeds of stimulus-driven BCIs are suboptimal due to the inherent limitations associated with relying on sensory stimulation, which generates highly variable neural responses, as well as the necessity of processing inherently noisy EEG data to extract the relevant neural information that is needed to control the BCI. Current BCI communication rates can potentially be improved with closed-loop optimisation techniques that exploit information from the user?s responses to previous stimuli to optimally tune the BCI system?s parameters to achieve the desired goal of maximising system performance under conditions of uncertainty. A closed-loop strategy can be used to select stimuli that are maximally informative of the user?s intent given the neural responses that are being measured, and I hypothesise that this data-adaptive approach to stimulus selection will minimise BCI decision errors and achieve better device control. Conventional BCIs use open-loop stimulus control methods as the stimulus presentation schedule is typically set in advance or occurs randomly, and there has been limited development of closed-loop stimulus paradigms in BCIs. The goal of the research that I propose is to investigate the feasibility of a novel closed-loop stimulus selection algorithm that will optimise the BCI stimulus presentation schedule in real-time based on the measured EEG data and the BCI system?s belief about the user?s intent, with proof-of-concept demonstrated in the P300 BCI speller. Specific Aim 1 will initially develop and test the novel algorithm in a non-disabled cohort to leverage the time efficiency and practicality of non-disabled participant studies to evaluate the real-time feasibility and potential utility of the closed-loop stimulus selection algorithm. Specific Aim 2 will test the closed-loop stimulus selection algorithm in individuals with ALS to assess the performance of the algorithm in a clinically relevant cohort. The successful development and testing of the proposed closed-loop stimulus selection algorithm in a challenging system such as the P300 BCI speller has the potential to instigate a paradigm shift towards closed-loop methods for BCI control and other applications where optimising system parameters in real-time to improve overall system performance could be of benefit.